Parameter Golf: What Really Works?

Title:Parameter Golf: What Really Works?

Abstract:How far can a language model improve under a strict artifact budget? Parameter Golf posed this question as an open community challenge in which participants trained the best language model, with the complete artifact (training code + compressed weights) required to fit within 16 MB and to be trained in under ten minutes on 8xH100 SXM GPUs. Quality was measured in bits-per-byte (BPB), the average number of bits required to encode each byte of unseen text. We analyze 2,037 pull requests and 1,430 clean-scored submissions from the contest, build a taxonomy of 84 optimization techniques, and measure each technique's contribution to BPB. The verified leaderboard score dropped from 1.2244 to 1.058 BPB across three phases, a 13.6% reduction, despite individual techniques rarely improving BPB by more than 1%. We show that most techniques' gains shrink when re-measured among competitive submissions, isolating the few methods that help regardless of the surrounding stack. Code and data are available at this https URL.

Computation and Language (cs.CL)

arXiv:2607.01517 [cs.CL]

(or arXiv:2607.01517v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2607.01517 Focus to learn more arXiv-issued DOI via DataCite

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